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We present the results of a detailed study on the detectability of the High Frequency Feature (HFF) in core-collapse supernova (CCSN) gravitational wave (GW) signals. We applied Residual Neural Networks (ResNet50), one of the state-of-the-art deep learning architectures in computer vision, to perform a multi-class classification of image samples built from time-frequency Morlet wavelet scalograms of LIGO-Virgo noise plus CCSN GW signals. We consider three target labels for three consecutive and mutually exclusive intervals in which the (first-order) slope of the HFF can be located. We optimized, trained, and tested the ResNet50 model with phenomenological waveforms. Next, we tested the optimized ResNet50 model with GW signals from CCSN simulations. At galactic distances of $1$Kpc and $5$Kpc with H1 and L1 data and $1$Kpc with V1 data, we obtained highly accurate results (test accuracies from $0.8933$ to $0.9867$), which show the feasibility of our methodology. In the case of further distances, we observed declines in test performance until $0.8000$ with H1 and L1 data at $10$Kpc and until $0.5933$ with V1 data at $10$Kpc. Without assuming the continuity and/or discontinuity of the HFF slope values, our methodology is general enough to address, at an early stage, the characterization of the HFF., Comment: 40 pages, 12 figures, 5 tables

We perform a novel multi-messenger analysis for the identification and parameter estimation of the Standing Accretion Shock Instability (SASI) in a core collapse supernova with neutrino and gravitational wave (GW) signals. In the neutrino channel, this method performs a likelihood ratio test for the presence of SASI in the frequency domain. For gravitational wave signals we process an event with a modified constrained likelihood method. Using simulated supernova signals, the properties of the Hyper-Kamiokande neutrino detector, and O3 LIGO Interferometric data, we produce the two-dimensional probability density function (PDF) of the SASI activity indicator and calculate the probability of detection $P_\mathrm{D}$ as well as the false identification probability $P_\mathrm{FI}$. We discuss the probability to establish the presence of the SASI as a function of the source distance in each observational channel, as well as jointly. Compared to a single-messenger approach, the joint analysis results in $P_\mathrm{D}$ (at $P_\mathrm{FI}=0.1$) of SASI activities that is larger by up to $\approx~40\%$ for a distance to the supernova of 5 kpc. We also discuss how accurately the frequency and duration of the SASI activity can be estimated in each channel separately. Our methodology is suitable for implementation in a realistic data analysis and a multi-messenger setting., Comment: 24 pages, 15 figures, accepted by PRD